Using Real-World Data to Determine Acute Chemotherapy Emetogenicity in Pediatric Patients
Bibliographic record
Abstract
PURPOSE: Direct pediatric information to inform chemotherapy emetogenicity in pediatric patients is limited. Therefore, the framework for antiemetic selection is uncertain. This study classified the acute emetogenicity of chemotherapy regimens in pediatric patients using data extracted from the electronic health record (EHR). METHODS: This retrospective, single-institution study extracted data from the EHR of patients age 0 to 18 years who received chemotherapy during an inpatient admission from July 1, 2018, through February 29, 2024. Data were organized by patient and chemotherapy block including patient demographics; date, time, and route of chemotherapy and antiemetic administration; and date and time of vomiting. When at least 30 patients received the same chemotherapy and antiemetics during a chemotherapy block, the proportion of chemotherapy blocks where patients experienced complete, partial, or failed chemotherapy-induced vomiting control was determined. Chemotherapy regimen emetogenicity was assigned using a revision of an accepted pediatric chemotherapy emetogenicity classification framework that adjusted for antiemetic administration. RESULTS: Seven thousand two hundred ninety-six chemotherapy blocks in 1,386 patients were identified. The emetogenicity of 25 chemotherapy regimens was classified: highly (7), moderately (5), low (10), and minimally (3) emetogenic. For 19 of these, no direct pediatric information was previously available. In five, our findings confirm the previous pediatric emetogenicity classification. Relative to emetogenicity classifications for adults, our findings led to classifications that were higher (seven regimens), lower (one regimen), or the same (four regimens). CONCLUSION: We have applied a novel method, EHR data extraction, to provide direct pediatric evidence to classify chemotherapy emetogenicity. Increasing the certainty of chemotherapy emetogenicity facilitates effective antiemetic selection for pediatric patients. This method may be applied in multi-institution studies to increase the number of chemotherapy regimens whose emetogenicity is classified using direct pediatric evidence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".